The distribution of antibiotic use and its association with antibiotic resistance

  1. Scott W Olesen
  2. Michael L Barnett
  3. Derek R MacFadden
  4. John S Brownstein
  5. Sonia Hernández-Diaz
  6. Marc Lipsitch
  7. Yonatan H Grad  Is a corresponding author
  1. Harvard TH Chan School of Public Health, United States
  2. University of Toronto, Canada
  3. Boston Children's Hospital, United States

Abstract

Antibiotic use is a primary driver of antibiotic resistance. However, antibiotic use can be distributed in different ways in a population, and the association between the distribution of use and antibiotic resistance has not been explored. Here we tested the hypothesis that repeated use of antibiotics has a stronger association with population-wide antibiotic resistance than broadly-distributed, low-intensity use. First, we characterized the distribution of outpatient antibiotic use across US states, finding that antibiotic use is uneven and that repeated use of antibiotics makes up a minority of antibiotic use. Second, we compared antibiotic use with resistance for 72 pathogen-antibiotic combinations across states. Finally, having partitioned total use into extensive and intensive margins, we found that intense use had a weaker association with resistance than extensive use. If the use-resistance relationship is causal, these results suggest that reducing total use and selection intensity will require reducing broadly-distributed, low-intensity use.

Data availability

State-level, aggregate antibiotic use and resistance data used in the main analyses are in Figure 3 - Source data 1 and 2. We do not own and cannot publish disaggregated MarketScan or Medicare data. MarketScan data are available by commercial license from Truven Health (marketscan.truvenhealth.com). Medicare data are available from ResDAC (www.resdac.org). ResDAC requires an application ensuring that requesting researchers comply with Common Rule, HIPAA, and CMS security and privacy requirements. Disaggregated ResistanceOpen data are restricted due to hospitals' privacy concerns. ResistanceOpen data are available by request from HealthMap (www.resistanceopen.org).

Article and author information

Author details

  1. Scott W Olesen

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5400-4945
  2. Michael L Barnett

    Department of Health Policy and Management, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
  3. Derek R MacFadden

    Division of Infectious Diseases, Department of Medicine, University of Toronto, Toronto, Canada
    Competing interests
    No competing interests declared.
  4. John S Brownstein

    Boston Children's Hospital, Boston, United States
    Competing interests
    No competing interests declared.
  5. Sonia Hernández-Diaz

    Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    No competing interests declared.
  6. Marc Lipsitch

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    Marc Lipsitch, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1504-9213
  7. Yonatan H Grad

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    For correspondence
    ygrad@hsph.harvard.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5646-1314

Funding

National Institute of General Medical Sciences (U54GM088558)

  • Scott W Olesen
  • Marc Lipsitch

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Neil M Ferguson, Imperial College London, United Kingdom

Version history

  1. Received: June 26, 2018
  2. Accepted: December 8, 2018
  3. Accepted Manuscript published: December 18, 2018 (version 1)
  4. Version of Record published: December 27, 2018 (version 2)

Copyright

© 2018, Olesen et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Scott W Olesen
  2. Michael L Barnett
  3. Derek R MacFadden
  4. John S Brownstein
  5. Sonia Hernández-Diaz
  6. Marc Lipsitch
  7. Yonatan H Grad
(2018)
The distribution of antibiotic use and its association with antibiotic resistance
eLife 7:e39435.
https://doi.org/10.7554/eLife.39435

Share this article

https://doi.org/10.7554/eLife.39435

Further reading

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease
    Scott W Olesen, Marc Lipsitch, Yonatan H Grad

    We are writing to reply to the comment by Pouwels et al., 2019 about our recent study (Olesen et al., 2018) on antibiotic use and antibiotic resistance.

    1. Ecology
    2. Epidemiology and Global Health
    Aleksandra Kovacevic, David RM Smith ... Lulla Opatowski
    Research Article

    Non-pharmaceutical interventions implemented to block SARS-CoV-2 transmission in early 2020 led to global reductions in the incidence of invasive pneumococcal disease (IPD). By contrast, most European countries reported an increase in antibiotic resistance among invasive Streptococcus pneumoniae isolates from 2019 to 2020, while an increasing number of studies reported stable pneumococcal carriage prevalence over the same period. To disentangle the impacts of the COVID-19 pandemic on pneumococcal epidemiology in the community setting, we propose a mathematical model formalizing simultaneous transmission of SARS-CoV-2 and antibiotic-sensitive and -resistant strains of S. pneumoniae. To test hypotheses underlying these trends five mechanisms were built into the model and examined: (1) a population-wide reduction of antibiotic prescriptions in the community, (2) lockdown effect on pneumococcal transmission, (3) a reduced risk of developing an IPD due to the absence of common respiratory viruses, (4) community azithromycin use in COVID-19 infected individuals, (5) and a longer carriage duration of antibiotic-resistant pneumococcal strains. Among 31 possible pandemic scenarios involving mechanisms individually or in combination, model simulations surprisingly identified only two scenarios that reproduced the reported trends in the general population. They included factors (1), (3), and (4). These scenarios replicated a nearly 50% reduction in annual IPD, and an increase in antibiotic resistance from 20% to 22%, all while maintaining a relatively stable pneumococcal carriage. Exploring further, higher SARS-CoV-2 R0 values and synergistic within-host virus-bacteria interaction mechanisms could have additionally contributed to the observed antibiotic resistance increase. Our work demonstrates the utility of the mathematical modeling approach in unraveling the complex effects of the COVID-19 pandemic responses on AMR dynamics.